Method and apparatus for determining alternans data of an ecg signal. The method can include determining at least one value representing at least one morphology feature of each beat of the ecg signal and generating a set of data points based on a total quantity of values and a total quantity of beats. The method can also include separating the data points into a first group of points and a second group of points and generating a feature map by plotting the first group of points and the second group of points in order to assess an alternans pattern of variation. The feature map can be analyzed by statistical tests to determine the significance difference between groups and clusters.
|
1. A method of determining alternans data of an ecg signal, the method comprising:
executing a software program stored in a memory with a processor, such that the executing step effectuates the following steps:
determining at least one value representing at least one morphology feature of each beat of the ecg signal, wherein the ecg signal is collected with a data acquisition device;
performing a statistical analysis using data points generated based on a total quantity of values and total quantity of beats the statistical analysis generating a confidence level;
determining whether the confidence level is significant;
processing the data points using a cluster analysis, the cluster analysis generating a first cluster of points and a second cluster of points;
separating the data points into a first group of points and a second group of points
comparing the first cluster of points with the first group of points;
comparing the second cluster of points with the second group of points; and
determining a value representative of a first set of matched points between the first cluster of points and the first group of points and a second set of matched points between the second cluster of points and the second group of points; and
wherein the confidence level is based at least in part on the value.
2. A method as set forth in
processing the data points using a paired t-test, the paired t-test generating a p-value, and
wherein the confidence level is based at least in part on the p-value.
3. A method as set forth in
4. A method as set forth in
determining a first center point corresponding to a first group of points and a second center point corresponding to a second group of points; and
determining a distance between the first center point and the second center point, the distance representing an estimated amplitude of the alternans data.
5. A method as set forth in
AmplitudeESTIMATE=√{square root over ((X1−X2)2+(Y1−Y2)2)}{square root over ((X1−X2)2+(Y1−Y2)2)}, wherein the first center point includes an X value (X1) and a Y value (Y1), and the second center point includes an X value (X2) and a Y value (Y2).
6. A method as set forth in
|
This application is a divisional of application Ser. No. 10/825,495 filed Apr. 15, 2004, now U.S. Pat. No. 7,072,709.
The present invention relates to cardiology, and more specifically to methods and apparatus for determining alternans data of an electrocardiogram (“ECG”) signal.
Alternans are a subtle beat-to-beat change in the repeating pattern of an ECG signal. Several studies have demonstrated a high correlation between an individual's susceptibility to ventricular arrhythmia and sudden cardiac death and the presence of a T-wave alternans (“TWA”) pattern of variation in the individual's ECG signal.
While an ECG signal typically has an amplitude measured in millivolts, an alternans pattern of variation with an amplitude on the order of a microvolt may be clinically significant. Accordingly, an alternans pattern of variation is typically too small to be detected by visual inspection of the ECG signal in its typical recorded resolution. Instead, digital signal processing and quantification of the alternans pattern of variation is necessary. Such signal processing and quantification of the alternans pattern of variation is complicated by the presence of noise and time shift of the alternans pattern of variation to the alignment points of each beat, which can be caused by limitation of alignment accuracy and/or physiological variations in the measured ECG signal. Current signal processing techniques utilized to detect TWA patterns of variation in an ECG signal include spectral domain methods and time domain methods.
In light of the above, a need exists for a technique for detecting TWA patterns of variation in an ECG signal that provides improved performance as a stand-alone technique and as an add-on to other techniques. Accordingly, one or more embodiments of the invention provide methods and apparatus for determining alternans data of an ECG signal. In some embodiments, the method can include determining at least one value representing at least one morphology feature of each beat of the ECG signal and generating a set of data points based on a total quantity of values and a total quantity of beats. The method can also include separating the data points into a first group of points and a second group of points and generating a feature map by plotting the first group of points and the second group of points in order to assess an alternans pattern of variation.
Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limited. The use of “including,” “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “mounted,” “connected” and “coupled” are used broadly and encompass both direct and indirect mounting, connecting and coupling. Further, “connected” and “coupled” are not restricted to physical or mechanical connections or couplings, and can include electrical connections or couplings, whether direct or indirect.
In addition, it should be understood that embodiments of the invention include both hardware and electronic components or modules that, for purposes of discussion, may be illustrated and described as if the majority of the components were implemented solely in hardware. However, one of ordinary skill in the art, and based on a reading of this detailed description, would recognize that, in at least one embodiment, the electronic based aspects of the invention may be implemented in software. As such, it should be noted that a plurality of hardware and software based devices, as well as a plurality of different structural components may be utilized to implement the invention. Furthermore, and as described in subsequent paragraphs, the specific mechanical configurations illustrated in the drawings are intended to exemplify embodiments of the invention and that other alternative mechanical configurations are possible.
The cardiac monitoring system 10 can acquire ECG data using a data acquisition module. It should be understood that ECG data can be acquired from other sources (e.g., from storage in a memory device or a hospital information system). The data acquisition module can be coupled to a patient by an array of sensors or transducers which may include, for example, electrodes coupled to the patient for obtaining an ECG signal. In the illustrated embodiment, the electrodes can include a right arm electrode RA; a left arm electrode LA; chest electrodes V1, V2, V3, V4, V5 and V6; a night leg electrode RL; and a left electrode leg LL for acquiring a standard twelve-lead, ten-electrode ECG. In other embodiments, alternative configurations of sensors or transducers (e.g., less than ten electrodes) can be used to acquire a standard or non-standard ECG signal.
A representative ECG signal is schematically illustrated in
The data acquisition module can include filtering and digitization components for producing digitized ECG data representing the ECG signal. In some embodiments, the ECG data can be filtered using low pass and baseline wander removal filters to remove high frequency noise and low frequency artifacts. The ECG data can, in some embodiments, be filtered by removing arrhythmic beats from the ECG data and by eliminating noisy beats from the ECG data.
The cardiac monitoring system 10 can include a processor and a memory associated with the processor. The processor can execute a software program stored in the memory to perform a method of the invention as illustrated in
As shown in
The processor can determine (at 102) a quantity [C] of values W representing a quantity [D] of morphology features F of a beat B (e.g., beat-one B1) of a quantity [G] beats, where [C] and [D] are each a quantity greater than or equal to one. In some embodiments, a single value W is determined for each morphology feature F (i.e., the quantity of [C] is equal to the quantity of [D]). However, in some embodiments, multiple values W are determined for a single morphology feature F and/or a single value W is determined for multiple morphology features F. Determining a quantity [C] of values W representing a quantity [D] of morphology features F can be repeated for a quantity [H−1] of beats of the quantity [G] of beats represented in the collected ECG data where a quantity [H] is greater than or equal to one and less than or equal to the quantity [G].
In some embodiments, any morphology features F of the beats B can be determined.
Other examples of morphology features that can be used include amplitude morphology features (e.g., an amplitude of a point representing the maximum down-slope of the curve formed by the data set representing the T-wave portion of a respective beat) and slope morphology features (e.g., a maximum positive slope of the curve formed by the data set representing the T-wave portion of a respective beat). Another example is mathematical model morphology features obtained by determining values representing a mathematical model of the curve formed by the data set representing the T-wave portion of a respective beat using, for example, a Gaussian function model, a power of Cosine function model, and/or a bell function model. A further example is time interval morphology features (e.g., a time interval between a maximum value and a minimum value of the data set representing a T-wave portion of a respective beat). Still another example is shape correlation morphology features obtained by determining a value representing a shape correlation of the curve formed by the data set representing the T-wave portion of a respective beat using, for example, a cross-correlation method and/or an absolute difference correlation method. An additional example is ratio morphology features (e.g., a ST:T ratio). Any other suitable morphology feature can be used in other embodiments of the invention. In some embodiments, as discussed above, the morphology feature can be determined using values of the data set(s) of the ECG data. In other embodiments, the morphology features can be determined using values representing the values of the data set(s) of the ECG data (e.g., a morphology feature of the first derivative of the curve formed by a respective data set).
Morphology features can be determined using an entire parsed data set as illustrated in
As shown in
A representative column-wise feature matrix A is illustrated in
As shown in
The matrix U can include the principal component vectors (e.g., the first principal component vector u1, the second principal component vector u2 . . . , the pth principal component vector up). The principal component vectors are also known as eigen vectors. The first principal component vector u1 can represent the most dominant variance vector (i.e., the first principal component vector u1 represents the largest beat-to-beat variance), the second principal component vector u2 can represent the second most dominant variance vector, and so on.
The S Matrix can include the principal components (e.g., the first principal component S1, the second principal component S2, . . . , the pth principal component Sp). The first principal component S1 can account for as much of the variability in the data as possible, and each succeeding principal component S can account for as much of the remaining variability as possible. The first principal component S1 can be used to determine alternans data (e.g., the square-root of the first PCA component S1 can provide an estimation of the amplitude of the most dominant alternans pattern of variation). In some embodiments, the second principal component S2 and the third principal component S3 can also provide useful alternans data.
The matrix V is generally known as the parameter matrix. The matrix V can be raised to a power of T. In other embodiments, the preprocessing of the feature matrix A can include other types of mathematical analyses.
The robustness of the preprocessing of the feature matrix A can be enhanced by increasing the quantity of [H] as the quantity of [D] increases. In other words, an increase in the number of morphology features F represented in the feature matrix A generally requires a corresponding increase in the number of beats B for which the morphology features F are being determined. The correspondence between the quantities of [D] and [H] is often based on the dependency between each of the [D] morphology features F. In some embodiments, the quantity of [H] is greater than or equal to 32 and less than or equal to 128. In other embodiments, the quantity of [H] is less than 32 or greater than 128. In some embodiments, the value of [H] is adaptively changed in response to a corresponding change in the level of noise in the measured ECG signal.
As shown in
Equations 1 and 2 shown below define an example of the mathematical functions Feature(beat+[N]) and Feature(beat), respectively. The first values of the points L determined using the mathematical function Feature(beat+[N]) can represent a difference feature QK+[N] and the second values of the points L determined using the mathematical function Feature(beat) can represent the difference feature QK, where K is a value equal to a beat (i.e., the beat for which the respective mathematical function is being used to determine either the first or second value of a point L).
Feature(beat+[N])=W(beat+2[N])−W(beat+[N])=QK+[N] [e1]
Feature(beat)=W(beat+[N])−W(beat)=QK [e2]
Tables 1-3 shown below represent the determination of points L using the mathematical functions Feature(beat+[N]) and Feature(beat) as defined in Equations 1 and 2 for [N]=1, 2, and 3, respectively. Equations 3 and 4 shown below define the mathematical functions Feature(beat+[N]) and Feature(beat) for [N]=1.
Feature(beat+1)=W(beat+2)−W(beat+1)=QK+1 [e3]
Feature(beat)=W(beat+1)−W(beat)=QK [e4]
Equations 5 and 6 shown below define the mathematical functions Feature(beat+[N]) and Feature(beat) for [N]=2.
Feature(beat+2)=W(beat+4)−W(beat+2)=QK+2 [e5]
Feature(beat)=W(beat+2)−W(beat)=QK [e6]
Equations 7 and 8 shown below define the mathematical functions Feature(beat+[N]) and Feature(beat) for [N]=3.
Feature(beat+3)=W(beat+6)−W(beat+3)=QK+3 [e7]
Feature(beat)=W(beat+3)−W(beat)=QK [e8]
As shown by Equations 3-8, the offset between the difference feature QK+[N] and the difference feature QK is dependent on the value of [N]. For [N]=l, the first value of the point L is determined by finding the difference between the value W of the second next beat BI+2 and the value W of the next beat BI+1, while the second value of the point L is determined by finding the difference between the value W of the next beat BI+1 and the value W of the current beat BI. For [N]=2, the first value of the point L is determined by finding the difference between the value W of the fourth next beat BI+4 and the value W of the second next beat BI+2, while the second value of the point L is determined by finding the difference between the value W of the second next beat BI+2 and the value W of the current beat BI. For [N]=3, the first value of the point L is determined by finding the difference between the value W of the sixth next beat BI+6 and the value W of the third next beat BI+3, while the second value of the point L is determined by finding the difference between the value W of the third next beat BI+3 and the value W of the current beat BI. Accordingly, the first values of the points L determined using the first mathematical function Feature(beat+[N]) are offset relative to the second values of the points L determined using the second mathematical function Feature(beat) by a factor of [N]. For example, for [N]=1, the first mathematical function Feature(beat+[N]) determines Feature(2) . . . Feature(Z+1) for beat-one B1 through beat-(Z) BZ, while the second mathematical function Feature(beat) determines Feature(1) . . . Feature(Z) for beat-one B1 through beat-(Z) BZ; for [N]=2, the first mathematical function Feature(beat+[N]) determines Feature(3) . . . Feature(Z+2) for beat-one B1 through beat-(Z) BZ, while the second mathematical function Feature(beat) determines Feature(1) . . . Feature(Z) for beat-one B1 through beat-(Z) BZ; for [N]=3, the first mathematical function Feature(beat+[N]) determines Feature(4) . . . Feature(Z+3) for beat-one B1 through beat-(Z) BZ while the second mathematical function Feature(beat) determines Feature(1) . . . Feature(Z) for beat-one B1 through beat-(Z) BZ. This offset relationship between the first values of the points L determined using the first mathematical function Feature(beat+[N]) and the second values of the points L determined using the second mathematical function Feature(beat) is further illustrated in Tables 1-3.
In Tables 1-3 shown below, the “Beat” column can represent respective beats B of the ECG signal and the “Feature Value” column can represent a value W of a morphology feature F of the corresponding respective beat B (e.g., an area morphology feature). As discussed above, the points L can be generated using values of other data corresponding to the determined values W. Also in Tables 1-3, an asterisk (*) represents an undetermined value of the point L (i.e., a value of the point L for which feature values W corresponding to beats B subsequent to the listed beats B1-B12 are required to determine the value of the point L), “f(b+N)” represents the mathematical function Feature(beat+[N]), and “f(b)” represent the mathematical function Feature(beat). Each point L shown in Tables 1-3 includes an X-value determined using the first mathematical function Feature(beat+[N]) and a Y-value determined using the second mathematical function Feature(beat).
TABLE 1
[N] = 1
Feature
f(b + N) = W(b+2N) − W(b+N)
f(b) = W(b+N) − W(b)
Feature Map
Beat
Value
f(b + 1) = W(b+2) − W(b+1)
f(b) = W(b+1) − W(b)
Point
Group
1
2
f(2) = 3 − 5 = −2
f(1) = 5 − 2 = 3
(−2, 3)
A
2
5
f(3) = 6 − 3 = 3
f(2) = 3 − 5 = −2
(3, −2)
B
3
3
f(4) = 2 − 6 = −4
f(3) = 6 − 3 = 3
(−4, 3)
A
4
6
f(5) = 4 − 2 = 2
f(4) = 2 − 6 = −4
(2, −4)
B
5
2
f(6) = 3 − 4 = −1
f(5) = 4 − 2 = 2
(−1, 2)
A
6
4
f(7) = 7 − 3 = 4
f(6) = 3 − 4 = −1
(4, −1)
B
7
3
f(8) = 3 − 7 = −4
f(7) = 7 − 3 = 4
(−4, 4)
A
8
7
f(9) = 5 − 3 = 2
f(8) = 3 − 7 = −4
(2, −4)
B
9
3
f(10) = 3 − 5 = −2
f(9) = 5 − 3 = 2
(−2, 2)
A
10
5
f(11) = 7 − 3 = 4
f(10) = 3 − 5 = −2
(4, −2)
B
11
3
f(12) = W13 − 7 = *
f(11) = 7 − 3 = 4
(*, 4)
A
12
7
f(13) = W14 − W13 = *
f(12) = W13 − 7 = *
(*, *)
B
TABLE 2
[N] = 2
Feature
f(b + N) = W(b+2N) − W(b+N)
f(b) = W(b+N) − W(b)
Feature Map
Beat
Value
f(b + 2) = W(b+4) − W(b+2)
f(b) = W(b+2) − W(b)
Point
Group
1
2
f(3) = 2 − 3 = −1
f(1) = 3 − 2 = 1
(−1, 1)
A
2
5
f(4) = 4 − 6 = −2
f(2) = 6 − 5 = 1
(−2, 1)
B
3
3
f(5) = 3 − 2 = 1
f(3) = 2 − 3 = −1
(1, −1)
A
4
6
f(6) = 7 − 4 = 3
f(4) = 4 − 6 = −2
(3, −2)
B
5
2
f(7) = 3 − 3 = 0
f(5) = 3 − 2 = 1
(0, 1)
A
6
4
f(8) = 5 − 7 = −2
f(6) = 7 − 4 = 3
(−2, 3)
B
7
3
f(9) = 3 − 3 = 0
f(7) = 3 − 3 = 0
(0, 0)
A
8
7
f(10) = 7 − 5 = 2
f(8) = 5 − 7 = −2
(2, −2)
B
9
3
f(11) = W13 − 3 = *
f(9) = 3 − 3 = 0
(*, *)
A
10
5
f(12) = W14 − 7 = *
f(10) = 7 − 5 = 2
(*, *)
B
11
3
f(13) = W15 − W13 = *
f(11) = W13 − 3 = *
(*, *)
A
12
7
f(14) = W16 − W14 = *
f(12) = W14 − 7 = *
(*, *)
B
TABLE 3
[N] = 3
Feature
f(b + N) = W(b+2N) − W(b+N)
f(b) = W(b+N) − W(b)
Feature Map
Beat
Value
f(b + 3) = W(b+6) − W(b+3)
f(b) = W(b+3) − W(b)
Point
Group
1
2
f(4) = 3 − 6 = −3
f(1) = 6 − 2 = 4
(−3, 4)
A
2
5
f(5) = 7 − 2 = 5
f(2) = 2 − 5 = −3
(5, −3)
B
3
3
f(6) = 3 − 4 = −1
f(3) = 4 − 3 = 1
(−1, 1)
A
4
6
f(7) = 5 − 3 = 2
f(4) = 3 − 6 = −3
(2, −3)
B
5
2
f(8) = 3 − 7 = −4
f(5) = 7 − 2 = 5
(−4, 5)
A
6
4
f(9) = 7 − 3 = 4
f(6) = 3 − 4 = −1
(4, −1)
B
7
3
f(10) = W13 − 5 = *
f(7) = 5 − 3 = 2
(*, *)
A
8
7
f(11) = W14 − 3 = *
f(8) = 3 − 7 = −4
(*, *)
B
9
3
f(12) = W15 − 7 = *
f(9) = 7 − 3 = 4
(*, *)
A
10
5
f(13) = W16 − W13 = *
f(10) = W13 − 5 = *
(*, *)
B
11
3
f(14) = W17 − W14 = *
f(11) = W14 − 3 = *
(*, *)
A
12
7
f(15) = W18 − W15 = *
f(12) = W15 − 7 = *
(*, *)
B
The difference feature Q is illustrated in
The difference feature Q is illustrated in
The difference feature Q is illustrated in
As shown by the “Group” column of Tables 1-3, each point L can be assigned to a respective group (e.g., group A or group B). The points L representing each odd beat (e.g., beat-one B1, beat-three B3, . . . , beat-eleven B11) can be assigned to a first group (i.e., group A), and the points representing each even beat (e.g., beat-two B2, beat-four B4, . . . , beat-twelve B12) can be assigned to a second group (i.e., group B). The points L can be assigned to group A and group B in this manner to represent a proposed odd-even alternans pattern of variation (i.e., ABAB . . . ). In other embodiments, the points L can be alternatively assigned to groups to represent other proposed alternans patterns of variation (e.g., AABBAABB . . . , AABAAB . . . , and the like).
As shown in
The feature map provides a visual indication of the divergence of the two groups of points, and thus the existence of a significant alternans pattern of variation. If there is a significant ABAB . . . alternans pattern of variation, the two groups of points will show separate clusters on the feature map (for example, as shown in
The feature map of
The feature map of
Although
In some embodiments, multiple feature maps can be generated for various quantities of [N] using the same set of values (e.g., the feature maps for [N]=1, 2, and 3, respectively, can be generated using the points determined in Tables 1-3). The display of multiple feature maps can further verify the existence of a significant alternans pattern of variation for the proposed alternans pattern of variation (e.g., a ABAB . . . alternans pattern of variation).
The operator can change the proposed alternans pattern of variation (i.e., change the grouping of the points to a different alternans pattern of variation) if the feature maps for [N]=1, 2, and 3 do illustrate differing divergence patterns for [N]=1 and 3 and [N]=2, respectively. For example, if the two groups of points diverge in the feature map for [N]=1 and 2, but not for the feature maps of [N]=3, the ECG signal represented by the values used to determine the points for the feature maps does not represent the proposed ABAB . . . alternans pattern of variation. However, the ECG signal can include a different alternans pattern of variation. Reassignment of the [E] points to different groups can be used to test a different proposed alternans pattern of variation.
As shown in
In some embodiments, a paired T-test can be performed on the first and second groups of points. A paired T-test is a statistical test which is performed to determine if there is a statistically significant difference between two means. The paired T-test can provide a p-value (e.g., p=0.001). In one embodiment, the confidence level is increased (i.e., a significant alternans pattern of variation exists) when the p-value is less than 0.001. In other embodiments, other suitable threshold levels can be established.
In some embodiments, a cluster analysis (e.g., a fuzzy cluster analysis or a K-mean cluster analysis) can be performed on the [E] points to determine a first cluster of points and a second cluster of points. The cluster analysis can also generate a first center point for the first cluster and a second center point for the second cluster. The first and second clusters of points can be compared with the first and second groups of points, respectively. A determination can be made of the number of clustered points that match the corresponding grouped points. For example, if point-one L1 and point-two L2 are clustered in the first cluster, point-three L3 and point-four L4 are clustered in the second cluster, point-one L1, point-two L2, and point-three L3 can be grouped in the first group, and point-four L4 can be grouped in the second group. Clustered point-three L3 does not correspond to grouped point-three L3, thereby resulting in a 75% confidence level. The confidence level can represent the percentage of clustered points that match the corresponding grouped points. In one embodiment, a confidence level about 90% can be a high confidence level, a confidence level between 60% and 90% can be a medium confidence level, and a confidence level below 60% can be a low confidence level. In other embodiments, the thresholds for the high, medium, and/or low confidence levels can be other suitable ranges of percentages or values.
As shown in
AmplitudeESTIMATE=√{square root over ((X1−X2)2+(Y1−Y2)2)}{square root over ((X1−X2)2+(Y1−Y2)2)} [e9]
The amplitude of the alternans pattern of variation often depends on the [D] morphology features used to determine the values W. Accordingly, the estimated amplitude is generally not an absolute value that can be compared against standardized charts. However, comparisons can be generated for estimated amplitudes of alternans patterns of variation based on the morphology features F that are determined and the processing step that is used.
As shown in
As shown in
Patent | Priority | Assignee | Title |
10092236, | Sep 25 2013 | ZOLL Medical Corporation | Emergency medical services smart watch |
10905335, | Sep 25 2013 | ZOLL Medical Corporation | Emergency medical services smart watch |
11678807, | Sep 25 2013 | ZOLL Medical Corporation | Emergency medical services smart watch |
8407173, | Jan 30 2008 | Aptima, Inc. | System and method for comparing system features |
Patent | Priority | Assignee | Title |
3554187, | |||
3658055, | |||
3759248, | |||
3821948, | |||
3902479, | |||
3952731, | Dec 15 1973 | Ferranti Limited | Cardiac monitoring apparatus |
4124894, | Oct 15 1974 | Hycel, Inc. | Apparatus and method for reporting detected error in a cardiac signal |
4136690, | Oct 31 1977 | Del Mar Avionics | Method and apparatus for vector analysis of ECG arrhythmias |
4170992, | Jan 05 1978 | Hewlett-Packard Company | Fiducial point location |
4181135, | Mar 03 1978 | American Optical Corporation | Method and apparatus for monitoring electrocardiographic waveforms |
4202340, | Sep 30 1975 | MIROWSKI FAMILY VENTURES L L C | Method and apparatus for monitoring heart activity, detecting abnormalities, and cardioverting a malfunctioning heart |
4316249, | Sep 28 1979 | LS MEDICAL INSTRUMENTS, INC | Automatic high speed Holter scanning system |
4417306, | Jan 23 1980 | Medtronic, Inc. | Apparatus for monitoring and storing utilizing a data processor |
4422459, | Nov 18 1980 | University Patents, Inc. | Electrocardiographic means and method for detecting potential ventricular tachycardia |
4432375, | May 24 1982 | Cardiac Resuscitator Corporation | Cardiac arrhythmia analysis system |
4457315, | Sep 18 1978 | Cardiac arrhythmia detection and recording | |
4458691, | Feb 11 1982 | Arrhythmia Research Technology, Inc. | System and method for predicting ventricular tachycardia by adaptive high pass filter |
4458692, | Feb 11 1982 | Arrhythmia Research Technology, Inc. | System and method for predicting ventricular tachycardia with a gain controlled high pass filter |
4475558, | May 28 1982 | Healthdyne, Inc. | System for providing short-term event data and long-term trend data |
4492235, | Feb 11 1982 | Arrhythmia Research Technology, Inc. | System and method for predicting ventricular tachycardia by derivative analysis |
4519395, | Dec 15 1982 | Medical instrument for noninvasive measurement of cardiovascular characteristics | |
4583553, | Nov 15 1983 | MEDICOMP, INC | Ambulatory ECG analyzer and recorder |
4589420, | Jul 13 1984 | SPACELABS, INC , A CORP OFCA | Method and apparatus for ECG rhythm analysis |
4603703, | Apr 13 1984 | BOARD OF RUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY THE, A BODY CORPORATE OF CALIFORNIA | Method for real-time detection and identification of neuroelectric signals |
4616659, | May 06 1985 | AT&T Bell Laboratories | Heart rate detection utilizing autoregressive analysis |
4665485, | Jul 22 1983 | Lundy Research Laboratories, Inc. | Method and apparatus for characterizing the unknown state of a physical system |
4679144, | Aug 21 1984 | FIRST FIDELTY BANK NATIONAL ASSOCIATION | Cardiac signal real time monitor and method of analysis |
4680708, | Mar 20 1984 | WASHINGTON UNIVERSITY, THE | Method and apparatus for analyzing electrocardiographic signals |
4732157, | Aug 18 1986 | MASSACHUSETTS INSTITUTE OF TECHNOLOGY, A CORP OF MA | Method and apparatus for quantifying beat-to-beat variability in physiologic waveforms |
4796638, | Sep 28 1984 | Kabushiki Kaisya Advance Kaihatsu Kenkyujo | Artifact detecting apparatus in the measurement of a biological signal |
4802491, | Jul 30 1986 | Massachusetts Institute of Technology | Method and apparatus for assessing myocardial electrical stability |
4832038, | Jun 05 1985 | The Board of Trustees of University of Illinois | Apparatus for monitoring cardiovascular regulation using heart rate power spectral analysis |
4854327, | Mar 07 1988 | ROBINSON, JOHN N | Non-invasive and continuous cardiac performance monitoring device |
4860762, | Sep 10 1986 | Koninklijke Philips Electronics N V | Dual channel resolver for real time arrythmia analysis |
4896677, | Dec 26 1987 | Fukuda Denshi Kabushiki Kaisha | Electrocardiographic waveform display apparatus, and method of expressing electrocardiographic waveforms |
4924875, | Oct 09 1987 | ASPECT MEDICAL SYSTEMS, INC | Cardiac biopotential analysis system and method |
4928690, | Apr 25 1988 | ZOLL Medical Corporation | Portable device for sensing cardiac function and automatically delivering electrical therapy |
4938228, | Feb 15 1989 | LIFEWATCH, INC | Wrist worn heart rate monitor |
4951680, | Sep 30 1987 | British Technology Group Limited | Fetal monitoring during labor |
4955382, | Mar 06 1984 | Ep Technologies | Apparatus and method for recording monophasic action potentials from an in vivo heart |
4958641, | Mar 10 1989 | CARD GUARD TECHNOLOGIES, INC | Heart data monitoring method and apparatus |
4972834, | Sep 30 1988 | VITATRON MEDICAL B V , A CORP OF THE NETHERLANDS | Pacemaker with improved dynamic rate responsiveness |
4974162, | Mar 13 1987 | UNIVERSITY OF MARYLAND, THE, A BODY CORPORATE OF MD | Advanced signal processing methodology for the detection, localization and quantification of acute myocardial ischemia |
4974598, | Apr 22 1988 | Heart Map, Inc. | EKG system and method using statistical analysis of heartbeats and topographic mapping of body surface potentials |
4977899, | Mar 10 1989 | CARD GUARD TECHNOLOGIES, INC | Heart data monitoring method and apparatus |
4979510, | Mar 06 1984 | EP TECHNOLOGIES, INC , A CORP OF CA | Apparatus and method for recording monophasic action potentials from an in vivo heart |
4989610, | Nov 16 1987 | SPACELABS, INC | Method and system of ECG data review and analysis |
5000189, | Nov 15 1989 | SOUTHERN FESTIVE FOODS, INC , A CORP OF GA | Method and system for monitoring electrocardiographic signals and detecting a pathological cardiac arrhythmia such as ventricular tachycardia |
5010888, | Mar 25 1988 | ARZCO MEDICAL ELECTRONICS, INC , A CORP OF DE | Method and apparatus for detection of posterior ischemia |
5020540, | Oct 09 1987 | ASPECT MEDICAL SYSTEMS, INC | Cardiac biopotential analysis system and method |
5025795, | Jun 28 1989 | ROBINSON, JOHN N | Non-invasive cardiac performance monitoring device and method |
5042497, | Jan 30 1990 | Cardiac Pacemakers, Inc. | Arrhythmia prediction and prevention for implanted devices |
5090418, | Nov 09 1990 | Del Mar Avionics | Method and apparatus for screening electrocardiographic (ECG) data |
5092341, | Jun 18 1990 | Spacelabs Healthcare LLC | Surface ECG frequency analysis system and method based upon spectral turbulence estimation |
5109862, | Mar 19 1990 | Del Mar Avionics | Method and apparatus for spectral analysis of electrocardiographic signals |
5117833, | Nov 13 1990 | ARRHYTHMIA RESEARCH TECHNOLOGY, INC | Bi-spectral filtering of electrocardiogram signals to determine selected QRS potentials |
5117834, | Aug 06 1990 | HARBINGER MEDICAL, INC | Method and apparatus for non-invasively determing a patients susceptibility to ventricular arrhythmias |
5148812, | Feb 20 1991 | Georgetown University | Non-invasive dynamic tracking of cardiac vulnerability by analysis of T-wave alternans |
5188116, | Feb 28 1991 | AURORA CAPITAL MANAGEMENT, L L C | Electrocardiographic method and device |
5201321, | Feb 11 1991 | BIO-DIMENSIONS, INC , | Method and apparatus for diagnosing vulnerability to lethal cardiac arrhythmias |
5234404, | Feb 19 1988 | Gensia Pharmaceuticals, Inc. | Diagnosis, evaluation and treatment of coronary artery disease by exercise simulation using closed loop drug delivery of an exercise simulating agent beta agonist |
5253650, | May 16 1989 | SHARP KABUSHIKI KAISHA, | Apparatus for recording an electrocardiogram |
5265617, | Feb 20 1991 | Georgetown University | Methods and means for non-invasive, dynamic tracking of cardiac vulnerability by simultaneous analysis of heart rate variability and T-wave alternans |
5277190, | Apr 07 1992 | The Board of Regents of the University of Oklahoma | Cycle length variability in nonsustained ventricular tachycardia |
5323783, | Nov 12 1992 | Spacelabs Healthcare, LLC | Dynamic ST segment estimation and adjustment |
5343870, | Nov 12 1991 | Quinton Instrument Company | Recorder unit for ambulatory ECG monitoring system |
5423878, | Mar 06 1984 | EP Technologies, Inc. | Catheter and associated system for pacing the heart |
5437285, | Feb 20 1991 | Georgetown University | Method and apparatus for prediction of sudden cardiac death by simultaneous assessment of autonomic function and cardiac electrical stability |
5560370, | Feb 20 1991 | Georgetown University | Method and apparatus for prediction of cardiac electrical instability by simultaneous assessment of T-wave alternans and QT interval dispersion |
5570696, | Jan 26 1994 | SPACELABS HEALTHCARE, INC | Method and apparatus for assessing myocardial electrical stability |
5713367, | Jan 26 1994 | SPACELABS HEALTHCARE, INC | Measuring and assessing cardiac electrical stability |
5792065, | Mar 18 1997 | Marquette Medical Systems, Inc. | Method and apparatus for determining T-wave marker points during QT dispersion analysis |
5819741, | Oct 07 1994 | Ortivus AB | Cardiac monitoring system and method |
5891045, | Jul 17 1997 | SPACELABS HEALTHCARE, INC | Method and system for obtaining a localized cardiac measure |
5921940, | Feb 20 1991 | Georgetown University | Method and apparatus for using physiologic stress in assessing myocardial electrical stability |
5935082, | Jan 26 1995 | SPACELABS HEALTHCARE, INC | Assessing cardiac electrical stability |
6169919, | May 06 1999 | BETH ISRAEL DEACONESS MEDICAL CENTER, INC | System and method for quantifying alternation in an electrocardiogram signal |
6389308, | May 30 2000 | System and device for multi-scale analysis and representation of electrocardiographic data | |
6453191, | Feb 18 2000 | SPACELABS HEALTHCARE, INC | Automated interpretation of T-wave alternans results |
6668189, | Oct 05 2001 | GE Medical Systems Information Technologies, Inc.; GE MEDICAL SYSTEMS INFORMATION TECHNOLOGIES, INC | Method and system for measuring T-wave alternans by alignment of alternating median beats to a cubic spline |
6850796, | Aug 31 1999 | MORTARA INSTRUMENT, INC | Method and apparatus to optimally measure cardiac depolarization/repolarization instability |
7072709, | Apr 15 2004 | GE Medical Information Technologies, Inc. | Method and apparatus for determining alternans data of an ECG signal |
20020138106, | |||
20030060724, | |||
DE2604460, | |||
DE3303104, | |||
DE4024360, | |||
EP80821, | |||
FR2539978, | |||
GB2070871, | |||
WO8102832, |
Executed on | Assignor | Assignee | Conveyance | Frame | Reel | Doc |
Apr 12 2004 | XUE, JOEL Q | GE MEDICAL SYSTEMS INFORMATION TECHNOLOGIES, INC | ASSIGNMENT OF ASSIGNORS INTEREST SEE DOCUMENT FOR DETAILS | 027069 | /0585 | |
Mar 30 2006 | GE Medical Systems Information Technologies, Inc. | (assignment on the face of the patent) | / |
Date | Maintenance Fee Events |
May 29 2015 | M1551: Payment of Maintenance Fee, 4th Year, Large Entity. |
Apr 23 2019 | M1552: Payment of Maintenance Fee, 8th Year, Large Entity. |
Apr 21 2023 | M1553: Payment of Maintenance Fee, 12th Year, Large Entity. |
Date | Maintenance Schedule |
Nov 29 2014 | 4 years fee payment window open |
May 29 2015 | 6 months grace period start (w surcharge) |
Nov 29 2015 | patent expiry (for year 4) |
Nov 29 2017 | 2 years to revive unintentionally abandoned end. (for year 4) |
Nov 29 2018 | 8 years fee payment window open |
May 29 2019 | 6 months grace period start (w surcharge) |
Nov 29 2019 | patent expiry (for year 8) |
Nov 29 2021 | 2 years to revive unintentionally abandoned end. (for year 8) |
Nov 29 2022 | 12 years fee payment window open |
May 29 2023 | 6 months grace period start (w surcharge) |
Nov 29 2023 | patent expiry (for year 12) |
Nov 29 2025 | 2 years to revive unintentionally abandoned end. (for year 12) |